Every analyst has inherited a spreadsheet that looked fine until the results did not. In finance, small data flaws can compound into large errors: a missing split adjustment, a duplicated row or a mislabeled currency can turn a promising backtest into fiction. A short, repeatable checklist catches most problems before they reach a model.

1. Check Completeness

Start by counting. How many rows and columns do you expect, and how many do you have? Look for missing values by column and by period. Gaps that cluster in certain dates or tickers often signal a broken data feed rather than random noise.

Decide in advance how missing values will be handled. Dropping them, filling forward and interpolating each carry different assumptions, and in a time series, filling forward can quietly create stale prices that look real.

2. Look for Duplicates

Duplicate records inflate counts and distort averages. Check for repeated rows and, more importantly, repeated keys such as the same ticker and date appearing twice with different values. Those conflicts reveal merges that went wrong or multiple vendors blended together.

3. Verify Date and Time Alignment

Time is the most frequent source of silent error. Confirm time zones, trading calendars and reporting lags. Fundamental data such as quarterly results is published weeks after the period ends. If a dataset records figures on the period-end date, a backtest may use information that was not yet available, a mistake known as look-ahead bias.

4. Confirm Units and Currency

Values may be in thousands, millions or raw units, and in different currencies across sources. Compare a few figures against a trusted reference to verify scale. A column that mixes dollars and local-currency values will produce nonsense averages without any error message.

5. Test for Outliers and Impossible Values

Scan distributions for extremes. Some outliers are real, such as a crash day, while others are errors, such as a negative share price or a daily return of several thousand percent. Use simple rules like range checks and comparison to neighboring values, then investigate flagged points manually instead of deleting them automatically.

6. Examine Corporate Actions and Adjustments

Stock splits, dividends, mergers and ticker changes alter how historical prices should be read. Check whether prices are adjusted and whether the adjustment method matches your purpose. Total-return analysis needs dividends; simple price charts may not. Also consider survivorship bias: a dataset containing only companies that exist today excludes those that failed, which flatters historical performance.

7. Reconcile Against an Independent Source

Whenever possible, compare totals or a sample of values with a second source. Reconciliation need not be exhaustive. Checking index levels, a few company filings or known aggregates quickly shows whether the data tracks reality. Differences are not always errors, since vendors use different conventions, but they must be understood.

Make the Checks Automatic

Manual inspection works once; automation works every time. Encode the checks as scripts or validation rules that run whenever new data arrives. Useful practices include:

• Fail loudly when row counts, date ranges or column names change unexpectedly.
• Log summary statistics for each load and compare them with previous loads.
• Keep raw data untouched and perform cleaning in documented, repeatable steps.
• Version datasets so that results can be reproduced later.

Document What You Found

A data dictionary that records each field’s meaning, source, unit and known limitations saves colleagues, and your future self, from repeating the same investigation. Note every cleaning decision and the reason for it. If a result is later questioned, this record is the difference between a quick explanation and a lost week.

The Bottom Line

Sophisticated models cannot rescue flawed inputs. Completeness, duplicates, timing, units, outliers, corporate actions and reconciliation form a compact checklist that catches the majority of real-world problems. Spending an hour on validation at the start is almost always cheaper than discovering the flaw after conclusions have been shared.